[Submitted on 15 Jul 2026]
Title:High-dimensional layer-state trajectories reveal the internal dynamics of transformer inference
View a PDF of the paper titled High-dimensional layer-state trajectories reveal the internal dynamics of transformer inference, by Heng Zhao and 3 other authors
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Abstract:Mechanistic studies of large language models localize function in parameters, attention heads or neurons, but those parts do not specify how one inference unfolds. We treat the ordered layer-state trajectory as the empirical process and test whether layer order defines a measurable computation. Across representative Qwen, Llama and Gemma checkpoints, realized order produced paths 1.60--2.24 times as aligned with their endpoint chord as endpoint-preserving shuffles. The order effect transferred to a lexical task, but was stronger in three random initializations of the same Qwen architecture, identifying an architecture-compatible scaffold rather than a training-specific law. Direct hidden states provided stronger mechanism coordinates than vocabulary readbacks, and a task-defined coordinate made candidate competition computable. After additive interventions failed, local transition actuators selectively controlled internal trajectories across checkpoints. A confidence gate then drove 17 of 87 held-out Qwen closure trajectories across the full-vocabulary top-1 candidate boundary while leaving all 203 non-closure top-1 outputs unchanged. Safety-matched action reassignment performed similarly, locating the gain in selective gating and the bounded actuator family rather than fine-grained dose assignment. Layer order exposes an observable, computable and behaviourally consequential process of transformer inference without establishing a complete theory of LLM dynamics or control of answer correctness.
Comments: Expanded preprint with 17 main figures and 13 supplementary figures. Source data and code are archived on Zenodo
Subjects:
Machine Learning (cs.LG); Computation and Language (cs.CL)
Cite as: arXiv:2610.26866 [cs.LG]
(or arXiv:2610.26866v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.26866
arXiv-issued DOI via DataCite
Submission history
From: Heng Zhao Edward [view email] [v1] Wed, 15 Jul 2026 00:02:19 UTC (1,695 KB)
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